Knowledge that Transforms

We propose a framework to identify and estimate earnings distributions and worker composition on matched panel data, allowing for two‐sided worker‐firm unobserved heterogeneity and complementarities in earnings. We introduce two models: a static model that allows for nonlinear interactions between workers and firms, and a dynamic model that allows, in addition, for Markovian earnings dynamics and endogenous mobility. We show that this framework nests a number of structural models of wages and worker mobility. We establish identification in short panels, and develop tractable two‐step estimators where firms are classified in a first step. Applying our method to Swedish administrative data, we find that log‐earnings are approximately additive in worker and firm heterogeneity. Our estimates imply the presence of strong sorting patterns between workers and firms, and a small contribution of firms—net of worker composition—to earnings dispersion. In addition, we document that wages have a direct effect on mobility, and that, beyond their dependence on the current firm, earnings after a job move also depend on the previous employer.

We study Pareto optimal policy reforms aimed at overhauling retirement financing as an integral part of the tax and transfer system. Our framework for policy analysis is a heterogeneous‐agent overlapping‐generations model that performs well in matching the aggregate and distributional features of the U.S. economy. We present a test of Pareto optimality that identifies the main source of inefficiency in the status quo policies. Our test suggests that lack of asset subsidies late in life is the main source of inefficiency when annuity markets are incomplete. We solve for Pareto optimal policy reforms and show that progressive asset subsidies provide a powerful tool for Pareto optimal reforms. On the other hand, earnings tax reforms do not always yield efficiency gains. We implement our Pareto optimal policy reform in an economy that features demographic change. The reform reduces the present discounted value of net resources consumed by each generation by about 7 to 11 percent in the steady state. These gains amount to a one‐time lump‐sum transfer to the initial generation equal to 10.5 percent of GDP.

We study stability of two-sided many-to-one matching in which firms' preferences for workers may exhibit complementarities. Although such preferences are known to jeopardize stability in a finite market, we show that a stable matching exists in a large market with a continuum of workers, provided that each firm's choice is convex and changes continuously as the set of available workers changes. We also study the existence and structure of stable matchings under preferences exhibiting substitutability and indifferences in a large market. Building on these results, we show that an approximately stable matching exists in large finite economies. We extend our framework to ensure a stable matching with desirable incentive and fairness properties in the presence of indifferences in firms' preferences.

Private information is at the heart of many economic activities. For decades, economists have assumed that individuals are willing to misreport private information if this maximizes their material payoff. We combine data from 90 experimental studies in economics, psychology, and sociology, and show that, in fact, people lie surprisingly little. We then formalize a wide range of potential explanations for the observed behavior, identify testable predictions that can distinguish between the models, and conduct new experiments to do so. Our empirical evidence suggests that a preference for being seen as honest and a preference for being honest are the main motivations for truth‐telling.

We propose a new solution for discrete exchange economies and resource‐allocation problems, the exclusion core. The exclusion core rests upon a foundational idea in the legal understanding of property, the right to exclude others. By reinterpreting endowments as a distribution of exclusion rights, rather than as bundles of goods, our analysis extends to economies with qualified property rights, joint ownership, and social hierarchies. The exclusion core is characterized by a generalized top trading cycle algorithm in a large class of economies, including those featuring private, public, and mixed ownership. It is neither weaker nor stronger than the strong core.

We propose a bootstrap‐based calibrated projection procedure to build confidence intervals for single components and for smooth functions of a partially identified parameter vector in moment (in)equality models. The method controls asymptotic coverage uniformly over a large class of data generating processes. The extreme points of the calibrated projection confidence interval are obtained by extremizing the value of the function of interest subject to a proper relaxation of studentized sample analogs of the moment (in)equality conditions. The degree of relaxation, or critical level, is calibrated so that the function of θ , not θ itself, is uniformly asymptotically covered with prespecified probability. This calibration is based on repeatedly checking feasibility of linear programming problems, rendering it computationally attractive. Nonetheless, the program defining an extreme point of the confidence interval is generally nonlinear and potentially intricate. We provide an algorithm, based on the response surface method for global optimization, that approximates the solution rapidly and accurately, and we establish its rate of convergence. The algorithm is of independent interest for optimization problems with simple objectives and complicated constraints. An empirical application estimating an entry game illustrates the usefulness of the method. Monte Carlo simulations confirm the accuracy of the solution algorithm, the good statistical as well as computational performance of calibrated projection (including in comparison to other methods), and the algorithm's potential to greatly accelerate computation of other confidence intervals.

In a transformation model , where the errors are i.i.d. and independent of the explanatory variables , the parameters can be estimated by a pseudo‐maximum likelihood (PML) method, that is, by using a misspecified distribution of the errors, but the PML estimator of is in general not consistent. We explain in this paper how to nest the initial model in an identified augmented model with more parameters in order to derive consistent PML estimators of appropriate functions of parameter . The usefulness of the consistency result is illustrated by examples of systems of nonlinear equations, conditionally heteroscedastic models, stochastic volatility, or models with spatial interactions.

We provide a nonlinear characterization of the macroeconomic impact of microeconomic productivity shocks in terms of reduced‐form nonparametric elasticities for efficient economies. We also show how microeconomic parameters are mapped to these reduced‐form general equilibrium elasticities. In this sense, we extend the foundational theorem of Hulten (1978) beyond the first order to capture nonlinearities. Key features ignored by first‐order approximations that play a crucial role are: structural microeconomic elasticities of substitution, network linkages, structural microeconomic returns to scale, and the extent of factor reallocation. In a business‐cycle calibration with sectoral shocks, nonlinearities magnify negative shocks and attenuate positive shocks, resulting in an aggregate output distribution that is asymmetric (negative skewness), fat‐tailed (excess kurtosis), and has a negative mean, even when shocks are symmetric and thin‐tailed. Average output losses due to short‐run sectoral shocks are an order of magnitude larger than the welfare cost of business cycles calculated by Lucas (1987). Nonlinearities can also cause shocks to critical sectors to have disproportionate macroeconomic effects, almost tripling the estimated impact of the 1970s oil shocks on world aggregate output. Finally, in a long‐run growth context, nonlinearities, which underpin Baumol's cost disease via the increase over time in the sales shares of low‐growth bottleneck sectors, account for a 20 percentage point reduction in aggregate TFP growth over the period 1948–2014 in the United States.

Endogenous demand composition across sectors due to income elasticity differences, or Engel's Law for brevity, affects (i) sectoral compositions in employment and in value‐added, (ii) variations in innovation rates and in productivity change across sectors, (iii) intersectoral patterns of trade across countries, and (iv) product cycles from rich to poor countries. Using a two‐country model of directed technical change with a continuum of sectors under nonhomothetic preferences, which is rich enough to capture all these effects as well as their interactions, this paper offers a unifying perspective on how economic growth and globalization affect the patterns of structural change, innovation, and trade across countries and across sectors in the presence of Engel's Law. Among the main messages is that globalization amplifies, instead of reducing, the power of endogenous domestic demand composition differences as a driver of structural change.

Fan, Liao, and Yao (2015) recently introduced a remarkable method for increasing the asymptotic power of tests in high‐dimensional testing problems. If applicable to a given test, their power enhancement principle leads to an improved test that has the same asymptotic size, has uniformly non‐inferior asymptotic power, and is consistent against a strictly broader range of alternatives than the initially given test. We study under which conditions this method can be applied and show the following: In asymptotic regimes where the dimensionality of the parameter space is fixed as sample size increases, there often exist tests that cannot be further improved with the power enhancement principle. However, when the dimensionality of the parameter space increases sufficiently slowly with sample size and a marginal local asymptotic normality (LAN) condition is satisfied, every test with asymptotic size smaller than 1 can be improved with the power enhancement principle. While the marginal LAN condition alone does not allow one to extend the latter statement to all rates at which the dimensionality increases with sample size, we give sufficient conditions under which this is the case.